---
title: "Does personal relevance attenuate citizens' persuasion by government branding? Evidence from a survey experiment on air-pollution policy"
author: '**Saar Alon-Barkat**'
date: " "
always_allow_html: yes
output:
  word_document: default
  pdf_document:
    toc: yes
  html_document:
    theme: flatly
    toc: yes
    toc_float:
      collapsed: no
      smooth_scroll: yes
      toc_depth: 3
link-citations: no
bibliography: phd_paper_2.bib
csl: https://raw.githubusercontent.com/citation-style-language/styles/master/apa.csl
urlcolor: blue
---

<br>

Draft, last edited at `r Sys.Date()`.

```{r set-global-options, echo = FALSE}
knitr::opts_chunk$set(eval = TRUE, 
                      echo = FALSE, 
                      message=FALSE,
                      warning = FALSE,
                      cache = FALSE)

```



```{r , include=FALSE, echo=FALSE}
#load("SVIVA_R_ENV.RData")
source("C:/SAAR/UNIVERSITY/R/SVIVA/code/experiment 2/SVIVA_exp2_dm_03.R")


```



```{r silent-packages}
library(knitr)
library(tidyverse)
library(stargazer)
library(car)
library(broom)
library(kableExtra)
library(ggthemes)
library(lmtest)
library(sandwich)
library(ggpubr)
library(effsize)
library(emmeans)
```


```{r}
N_raw = nrow(SVIVA2_raw_00)
N_0=SVIVA2_00%>%nrow()
N_1=SVIVA2_01%>%nrow()

filter_IP = N_0-(SVIVA2_00 %>%
  distinct(IP,.keep_all=TRUE)%>%nrow())
filter_age = N_0-(SVIVA2_00 %>%
  filter(!(AGE%in% 1:17))%>% nrow())
filter_IMC = N_0-(SVIVA2_00 %>%
  filter(IMC==1)%>% nrow())
filter_time = N_0-(SVIVA2_00 %>%
  filter(TIMER_total>=3,TIMER_total<=30)%>%nrow())

N_1_haifa = filter(SVIVA2_01,AREA==1)%>%nrow()
N_1_center = filter(SVIVA2_01,AREA==0)%>%nrow()



```


```{r}
#Figures and tables
table.x=0
figure.x=0
```

<br>

---

# Abstract

*Recent studies have demonstrated the potency of government branding to positively affect citizens' judgments of government organizations and policies. Additionally, studies have pointed to the detrimental implications of the emotive effect of symbols, mainly its ability to compensate for organizations' poor functioning, and accordingly to elicit undue trust. In light of these concerns, this study explores the boundaries of governments' persuasion of citizens through branding and symbolic communications. Building on social psychology and marketing research, I hypothesize that citizens are less susceptible to persuasion by symbolic communication, the more they perceive the policy issue in the communication as personally relevant. I test this expectation through a survey experiment, focused on air-pollution policy in Israel, exploiting the natural variation in the perceived personal relevance between citizens residing in a polluted area in the country and others. The results indicate that even high levels of perceived personal relevance do not significantly attenuate the effect of symbols, which entails that the boundaries of persuasion and manipulation through branding are wider than expected.*


<br>

*Keywords: Government communications; Government branding; Trust; Experiment*

---


<br>

# Introduction

Public Administration scholarship has shown a growing interest in public sector branding, and its potency to influence citizens' perceptions and direct their behavior [@eshuis_2012; @marland_2016; @marland_2017; @zavattaro_website_2018; @sataen_2015]. Congruently, recent empirical studies established that symbolic, brand elements entangled in government communications (e.g. brand names, logos, colors and figures) can have a positive emotional effect on citizens, which leads them to view public organizations and their policies and services more favorably [@marvel_2015a; @karens_2016; @alonbarkat_2017; @teodoro_2018]. This growing scholarly interest corresponds with the overall increase in the use of branding practices by public-sector organizations over the past decade or so, in part due to the expansion of digital communication and social media [@eshuis_2012; @marland_2017; @mickoleit_2014; @mergel_2013]. 

The ability of government organizations to modify citizens' attitudes via such symbolic communications can be perceived favorably, insofar as it mitigates citizens' distrust in organizations and undervaluation of their performance. Accordingly, symbolic communications, as opposed to the delivery of mere factual information and substantive arguments, may enable organizations to overcome citizens' negative prior beliefs and biases against the public sector [@marvel_2015a; @marvel_2015; @hvidman_2016; @hvidman_2019]. Nonetheless, this form of persuasion is also highly problematic. Normatively, we expect citizens in a democratic society to form their opinions about their government based on their critical thinking, rather than on unconscious emotive responses. A yet more serious concern regards the potency of symbolic communication to compensate for organizations' poor performance, poorly planned policy plans and logically unpersuasive explanations, as indicated by previous studies [@alonbarkat_2017, AUTHOR]. Congruently, government branding can yield a misalignment between citizens' positive view and the "real world", which would undermine governments' democratic accountability and responsiveness. Moreover, government agencies and/or politicians controlling them could exploit branding and symbolic communication as an instrument of propaganda, and seek to manipulate public opinion and mitigate justified public criticism of agencies' failures.  

The abovementioned concerns of the negative implications of branding for democracy are the main motivation of this study. These concerns necessitate a better theoretical understanding of the boundaries of governments' persuasion (and possibly manipulation) of citizens through symbolic communication, which would enable us to asses more effectively the scope of the risk that these practices pose for democracy. In line with this motivation, this study focuses of an important potential limitation that regards the *perceived personal relevance of the policy issue*. I explore whether the effect of symbolic elements in communications is restricted to policy issues that are not perceived by citizens as having a significant consequence for their personal lives, or rather that citizens are similarly susceptible to persuasion via branding even with regard to such personally relevant issues. This moderating factor is particularly important, since we tend to expect these affected groups to pay more attention to government actions that concern their interests, and to hold them to account for their consequences to a greater extent. That expectation can be supported, theoretically, by the social psychology Elaboration Likelihood Model (ELM) [@petty_1986; @petty_2011]. The model postulates that the more people perceive the communication as personally relevant, the more inclined they are to scrutinize its message, and accordingly the less likely they are to rely on peripheral, symbolic elements. 

Employing a survey experiment, I test this latter expectation in the empirical context of the Israeli Environment Protection Ministry, and its policy regarding air-pollution in a specific area -- the Haifa Bay. The research sample (*n* = `r SVIVA2_01 %>% nrow()`) consists of citizens residing in the polluted area, matched with residents of other cities in the center of the country that are not exposed to high levels of air-pollution. I present all participants a policy plan regarding the air-pollution in the Haifa Bay area, while experimentally varying both its appearance (familiar symbolic elements from the Ministry's communications versus two control groups) and substantive information (logically persuasive versus unpersuasive policy plans). I examine the effects of these manipulations on participants' trust in the policy plan across the two areas, and test the hypothesis that residents of the polluted area are less affected by familiar symbolic elements (and more affected by the differences in substantive arguments). I further supplement this observational comparison between the areas with an experimental treatment for the perceived personal relevance of the policy, and with additional robustness tests. 

Contrary to my initial expectations, the results consistently indicate that even high levels of perceived personal relevance do not significantly attenuate the effect of symbols on citizens' trust. These findings tend to suggest that the boundaries of the effect of symbolic communication are wider than expected. I discuss the theoretical and normative implications of these findings. 

<br>

# Persuasion by symbols and its moderation by perceived personal relevance

A brand can be defined as *"a symbolic construct that consists of a name, term, sign, symbol, or design, or combination of these, created deliberately to identify and phenomenon and differentiate it from similar phenomena by adding particular meaning to it"* [@eshuis_2012, p. 19]. Branding, therefore, can be considered a form of symbolic communication. In practice, symbolic elements that are strategically designed to evoke positive emotions and associations, can be found in almost every government communication with citizens. 

Symbolic elements entangled in communications can affect citizens' attitudes through a psychological mechanism of evaluative conditioning, whereby the positive associations triggered by the symbols are unconsciously transferred to the public organization [@alonbarkat_2017; @dehouwer_2012]. So for instance, when a public organization uses a celebrity endorser in a public campaign, the positive connotations for the celebrity (e.g. physically attractive, funny, talented) can be extended to the organization, which would lead citizens to also view the organization and its actions more favorably. This mechanism is considered a primitive form of persuasion which requires very little cognitive effort, as opposed to persuasion resulted by thoughtful consideration of substantive arguments. Thus, the question of under which circumstances are citizens' more/less susceptible to persuasion via branding, can be linked to a more general discussion in social psychology regarding the relations between different psychological processes of persuasion through communication, and the determinants and moderators of these different processes. 

According to the Elaboration Likelihood Model (ELM) [@petty_1986; @petty_2011], persuasion can occur via different process that can be placed along an "elaboration continuum". At the high end of that continuum, also called "central route", persuasion occurs through processes that require high degrees of thought and cognitive effort. This type of processing involves thoughtful scrutiny of arguments in the communication that are central to the merits of the issue. People located at the high end tend are more likely to form their judgements on the matter based on the quality of the arguments. They are more likely to differentiate between strong and weak arguments, and would be persuaded mainly by strong, logically persuasive arguments. As for those who are located at the low end of that continuum, also called "peripheral route", persuasion may occur mainly through processes that require little amount of thinking, such as evaluative conditioning. Another prominent example for peripheral route processing is people's reliance on message source as a heuristic cue [@james_2017a]. When people are located between the two ends of the continuum, a mixture of these persuasion processes will operate.

In any given situation, the position of people along that continuum (and accordingly the dominant type of processing) would be determined by their motivation and ability to scrutinize the message. The more motivated people are to elaborate on the message and the more capable they are of doing so, the more likely they are to focus on the substantive arguments, and likewise the less susceptible they are to persuasion via peripheral route processes. ELM literature has pointed to a variety of factors that may shape people's motivation and ability to think about a message, and accordingly affect their inclination to be persuaded either by the arguments or by peripheral cues. Perhaps the most studied factor in this regard is the perceived personal relevance of the communication (or "issue involvement"), namely the extent to which they perceive the issue at hand as having significant consequences for their own lives. That variable has been considered the most important determinant of motivation to scrutinize the message [@petty_2002; @petty_2011].[^footnote_elm_factors] It has been theorized that the more people perceive an issue as personally relevant, the more motivated they are to invest in thinking about it, since the consequences of being incorrect are greater [@petty_1979; @petty_1981; @petty_1983; @johnson_1989; @petty_1990; @burnkrant_1989]. Accordingly, experimental studies in marketing and social psychology have showed that individuals are more affected by peripheral cues and less affected by argument quality, the less they perceive the issue as personally relevant. 

For instance, in a study by @petty_1983, participants were asked to express their attitudes about a new product (disposable razor), after being exposed to a magazine ad. The ad involved either a celebrity endorser or a non-famous endorser (a symbolic peripheral cue) and contained substantive information about the product that represent either strong or weak arguments about its quality. In addition, scholars manipulated the perceived personal relevance of the product by telling subjects either that the product would soon be available in their local area (high relevance), or that it would be available in other areas (low relevance). The results of the study indicated that the celebrity endorser had a positive effect on participants' attitudes about the product under the low personal relevance condition (estimated as roughly 0.7 SDs), but had no effect under the high personal relevance condition. Likewise, the effect of argument quality was more than three times greater for the high personal relevance group.  

[^footnote_elm_factors]: For a thorough review of motivational and ability factors affecting likelihood to elaborate see @petty_1998.

Viewed against the above theoretical framework, symbolic elements in government communications can be regarded as peripheral cues, which can affect citizens' attitudes though a peripheral route process of evaluative conditioning.[^footnote_symbols_arguments] Hence, based on ELM, citizens judgements about public organizations are less likely to be affected by symbols (and more likely to be shaped by substantive arguments) the more motivated they are to critically think about the message. Citizens' level of motivation is likely to be determined, to a great extent, by their the degree in which they perceive the policy issue in the communication as having personal relevance for themselves.

[^footnote_symbols_arguments]: In certain cases, symbolic elements can be also regarded as arguments. For instance, slogans normally convey symbolic meaning, but also contain a substantive message.  

In sum, I derive from social psychology literature of persuasion through communication the following moderation hypotheses: 

*H<sub>1</sub> - Symbolic elements in communications are likely to have a greater positive effect on citizens' attitudes about government organizations and policies, the lower the perceived personal relevance of the communication.*

*H<sub>2</sub> - The argument quality of the information in communications is likely to have a weaker effect on citizens' attitudes about government organizations and policies, the lower the perceived personal relevance of the communication.*

<br>

# Methodology

The above-mentioned theoretical expectations are tested via a randomized survey experiment, building on the methodology of @petty_1983. The experiment focuses, empirically, on the case of the Israeli Environmental Protection Ministry (hereafter: EPM), and its policy for reducing the air pollution in the highly polluted Haifa Bay area. In the following section, I describe the empirical case, data collection, experimental design and operationalization of research variables.[^footnote_other_paper]

[^footnote_other_paper]: The experiment was additionally designed to test a set of hypotheses regarding the interaction between symbols and information in communications. The methodological aspects which are relevant for examining the additional hypotheses are described in a separate publication. In this paper, I focus on those methodological and empirical parts of the study that are consequential for the examination of the moderating role or perceived personal relevance. A summary statistics and correlation of all variables, as well as the full survey are available in the supplementary Appendix (sections 5, 8), and replication materials are available in a github repository [url to be added upon publication].

EPM is a national-level bureau responsible for the formulation, coordination, and execution of environmental policy in Israel. Among others, EPM is responsible for monitoring and regulating the air quality across the country. The Haifa Bay area, located in the north of Israel, has been characterized by relatively high levels of air pollution, mainly due to the Haifa Bay industrial zone, which is located in proximity of a number of urban residential areas. It is considered the largest industrial zone in the country, with concentration of petrochemical industries, including oil refineries, power plants, and chemical factories. Beginning in Mid 2000s, EPM has been developing and implementing a series of policy programs to monitor and reduce the high levels of pollution specifically in that area [@theministryofenvironmentprotection_2015; @theministryofenvironmentprotection_2015a; @theministryofenvironmentprotection_2019]. While EPM has claimed that its programs have been effective and led to a significant reduction in air-pollution levels, local residents, professionals and other public agencies have remained highly concerned about the issue, and  accordingly it remained high on the public agenda [@statecomptrollerofisrael_2019]. Specifically, the issue received much media and public attention in 2015-2016, following a series of reports and media publications about the health consequences of the air-pollution to Haifa Bay residents. In 2015, the Ministry of Health published statistical data indicating that Haifa Bay residents experience significantly higher rates of cancer and asthma, compared to other areas of the country. Thereafter, in 2016, media reporters revealed the interim results of a large epidemiological study, establishing a link between air-pollution in the Haifa Bay and certain types of cancer, as well as abnormal measurements of newborn babies and growth curves of infants.[^footnote_epidemiological_study] The media reports of that study repeatedly highlighted a particular finding that babies in the Haifa Bay are born with "smaller heads" [e.g. @koriel_website_2016; @rivlin_website_2016]. These publications, and others, caused much panic among the local population, and especially among young parents. 

[^footnote_epidemiological_study]: The epidemiological study was originally sponsored by Ministry of Health. The study was planned to be conducted over five years, between 2015-2020. The findings, which leaked to the media, regarded the interim results of the first year of the study. Following that publication, the study has been significantly criticized by the local research community as well as the professional steering committee of the research. As a result, in 2017, the Ministry decided to cancel its involvement in that research.    

The air-pollution in the Haifa Bay offers a fortunate case for examining the moderating role of perceived personal relevance. The distinction between those who reside in the polluted area and those who do not entails a natural variation in that moderating variable. Importantly, because this policy issue is intangible and scientifically complicated, the variation in personal relevance is not strongly confounded with personal knowledge about the issue, as is normally the case [@petty_1983]. Accordingly, those residing in the polluted Haifa Bay area are likely to be more motivated to scrutinize a government communication about the air-pollution in their area, yet they are not necessarily more capable of critically evaluating its content. That latter assumption was the main reason for selecting that case.  

While comparing those residing in the Haifa Bay to others enables me to examine the moderating effect of perceived personal relevance, it still has an important limitation -- it represents an observational comparison. The differences in personal relevance can be theoretically confounded by unobserved covariates, that cannot be effectively controlled in the analyses. I address this limitation in the research design in three main ways. *First*, in the selection of the research population, I sought to reduce differences in socioeconomic and demographic variables between residents of Haifa Bay, and their comparison group. Accordingly, I selected the major cities in the Haifa Bay area (*Haifa*, *Nesher*, *Qiryat-Hayiim*,[^footnote_qiryat-Hayiim] *Qiryat-Bialik*, *Qiryat-Mozkin*, *Qiryat-Ata* and *Qiryat-Yam*), and matched them with citizens from cities with a similar profile and population size at the center of the country (*Natania*, *Petach-Tikva* and *Rishon-Lezion*). The socioeconomic characteristics of these selected cities are summarized in the supplementary appendix (section 1). I further searched relevant EPM documents and news websites and confirmed that there were no reports of high air-pollution levels in the three center cities. 

*Second*, in addition to the policy regarding the Haifa Bay air-pollution, I also included in the experiment a second policy plan that is expected to be perceived by the two groups as equally relevant. For this purpose, I selected a policy plan of EPM that regards *reducing domestic waste and increasing recycling* (henceforth: recycling policy). The comparison between the two policies is intended to confirm that respondents’ divergent responses to symbolic elements in the communication of the Haifa Bay air-pollution policy, if found, can be attributed to differences in perceived personal relevance, and not to other covariates. *Third*, in addition to the observational comparison between the two areas, I also included an experimental manipulation aimed at enhancing the perceived personal relevance of the air-pollution policy among the Haifa Bay residents.  

[^footnote_qiryat-Hayiim]: Qiryat-Hayiim is considered a neighborhood within the jurisdiction of Haifa, although it is geographically separated from the rest of the city, and people tend to distinguish it, informally. 


## *Data collection and Procedure*

I was assisted by an Israeli internet research panel company *iPanel* for the selection and recruitment of residents of these selected cities. The survey experiment was conducted online through Qualtrics Survey Software. The online survey link was sent by *iPanel* between 28.1-1.2.2018. A total of `r N_0` respondents completed the survey (a response rate of `r round((N_0/5077)*100,1)`%). I further filtered out `r N_0-N_1` observations (`r round(((N_0-N_1)/N_0)*100,1)`%) due to following criteria: multiple entries from the same IP address (n=`r filter_IP`), failure at the instructional manipulation test (n=`r filter_IMC`), submitting the survey in less than 3 or more than 30 minutes (n=`r filter_time`), and reported age under 18 (n=`r filter_age`). These filters are neither associated with the areas nor with the experimental conditions. Following this screening, I ended up with a sizable analytical sample of `r N_1` subjects, of whom `r SVIVA2_01_haifa%>%nrow()` live in the Haifa Bay cities, and `r SVIVA2_01_center%>%nrow()` live in the center cities.[^footnote_unfiltered_sample]

[^footnote_unfiltered_sample]: In the supplementary appendix (section 7), I replicate the main models on the unfiltered sample, with no significant change to the results. 

The survey was presented to the participants as an academic study about citizens' attitudes regarding environmental policies in general. After a set of pre-manipulation questions, all subjects, regardless of their area of residence, were randomly assigned to treatment and control conditions of perceived personal relevance (hereafter: *relevance manipulation*). Subjects in the relevance treatment group were asked three questions about their perceptions of environmental policy in general. They were then asked to state their area and locality of residence, and to describe, in their own words, the most disturbing environmental issue in their area. These questions were designed to prime those residing in the Haifa Bay area to think about the personal relevance of air-pollution in their area.[^footnote_relevance_manipulation] Subjects in the relevance control group were asked equivalent questions about their field of occupation.

[^footnote_relevance_manipulation]: The first three questions are: "To what extent are you interested in environmental issues?"; "To what extent do you follow environmental issues in the media and/or in social networks?"; "Compared to the current situation, do you think the government should invest more or less in dealing with environmental issues?" These questions were initially designed to prime participants to think not only about the relevance of the Haifa Bay air-pollution, but of environmental issues general. Yet, I focus here on the moderation of the relevance manipulation on Haifa Bay citizens' responses to the air-pollution policy. In additional analyses, I also examine the moderating effect of the relevance treatment for center subjects’ responses to Haifa Bay air-pollution policy, as well as for the recycling policy (for the entire sample).

Participants were then presented with the two policy plans (Haifa Bay air-pollution and recycling), displayed in a random order. They were informed that these plans were obtained from EPM's annual work plan for the following year. The participants were randomly assigned to one of three conditions of symbolic elements in the communication (hereafter: *symbols manipulation*): treatment ("real symbols"), control-"no symbols" and control-"fake symbols". In the real symbols condition, the two policies are displayed with three symbolic brand elements, which have been used in EPM's public communications, and became widely recognized by the public. The first symbolic element is the unique brand logo of the EPM, which consists a pair of green and orange leaves that resemble two hands, designed to symbolize peace and harmony with the environment. The second element is the green color, which is strongly associated with taking care of the environment. The third element is the images of celebrity comedians from advertising campaigns of the EPM. The Haifa Bay air-pollution policy (in the real symbols condition) was presented with an image from EPM campaign from 2010 under the slogan "starting to think green", starring the Israeli celebrity comedian Tal Friedman. The recycling policy (in the real symbols condition) was presented with an image from a campaign from 2017 for reducing the use of disposable bags ("taking every bag seriously"), starring the comedian Ido Rosenblum. In the control no-symbols condition, the two policies are displayed in a minimal black and white design, and without the logo and the celebrities’ images. Finally, in the control fake-symbols condition, the EPM logo is replaced by a fake logo, the green color is replaced with blue, and the images of celebrities from the campaigns are replaced with edited images of unfamiliar people. The purpose of the fake-logo condition was to distinguish the effect of the symbolic meaning of the EPM's graphic brand elements (i.e. the symbolic effect) from a possible effect of their aesthetic design. Accordingly, I specifically designed the "fake" symbols so that they would resemble the real symbols and have equivalent aesthetic qualities, without activating particular emotions and associations related to EPM, or environmental policy. Hence, these elements can be also regarded as peripheral cues (according to ELM conceptualization), yet they do not represent familiar symbols of EPM. The symbols conditions are displayed in **Appendix A**. Further details about the real and fake symbols are found the supplementary appendix (section 2). 
 
In addition, I also included a manipulation of the argument quality of the information in the communication about the policy plan (hereafter: *information manipulation*). Per each policy, subjects were randomly allocated to one of two conditions of information: strong policy (i.e. logically persuasive policy plan) or weak policy (i.e. logically unpersuasive). Each subject was exposed to two policy plans, one strong and one weak, displayed in a random order. The information in the strong policy plan condition was taken from the EPM’s real work plan and from other official sources, whereas the information in the weak policy plan was fictional, and deliberately designed to create a poor policy plan, which is incompatible with achieving the policy goals.[^footnote_reliability_information_manipulation] The full texts of the weak and strong conditions for the two policy plans is displayed in **Appendix B**.

[^footnote_reliability_information_manipulation]: A possible concern about that design is that participants would question the reliability of the weak policy. I assess that concern in the supplementary appendix (section 3.2). 

Each policy plan was followed by six questions about respondents' trust in the policy. After the trust questions of the second policy, the subjects were asked additional questions about the two policies, including the degree to which they perceive each of them as personally relevant. These questions were followed by an Instructional Manipulation Check item, intended to assess participants’ diligence. The last sections included a set of demographic questions including questions about their place of residence and work, and manipulation checks for participants' familiarity with the real symbols. At the end of the survey, the participants were briefed about the manipulations, and the possible deception. 

In line with the abovementioned hypotheses, my main focus in this paper is on participants' trust in the Haifa Bay air-pollution policy, and the two-way interactions between residence in Haifa Bay and the symbols and information manipulations of that policy. I also explore these interactions with regard to the recycling policy, and compare them to the air-pollution interactions. Additionally, within the Haifa Bay sample, I examine the two-way interactions between the relevance manipulation and the symbols and information manipulations. 

I further conducted power estimations for the main effects of the symbols and information manipulations, their conditional effects within each area and the aforementioned interactions. I conducted these estimations after the data collection and analyses, based on the analytical sample size. The estimated detectable (power=0.8) interaction coefficient between areas and the real symbols treatment is 0.27 SDs, and between areas and weak information manipulation is 0.23 SDs. As for the interactions with the relevance manipulation (among Haifa Bay respondents), I estimate that the detectable coefficients of their interactions with the symbols and information conditions are 0.36 and 0.29 SDs, respectively. The power estimations are reported in details in the supplementary appendix (Section 4). 


## *Variables*


```{r}
alpha_air_trust = psych::alpha(select(SVIVA2_01, TRUST_air_q1,
                               TRUST_air_q2,
                               TRUST_air_q3,
                               TRUST_air_q4,
                               TRUST_air_q5,
                               TRUST_air_q6))[["total"]][["raw_alpha"]]%>%round(2)


alpha_waste_trust = psych::alpha(select(SVIVA2_01, TRUST_waste_q1,
                               TRUST_waste_q2,
                               TRUST_waste_q3,
                               TRUST_waste_q4,
                               TRUST_waste_q5,
                               TRUST_waste_q6))[["total"]][["raw_alpha"]]%>%round(2)

alpha_trust_comb = ((alpha_air_trust+alpha_waste_trust)/2)%>%round(2)



relevance.areas.d <- cohen.d(SVIVA2_01$RELEVANCE_air_obs~SVIVA2_01$AREA_center)
relevance.exp.d <- cohen.d(RELEVANCE_air_obs~RELEVANCE_exp, SVIVA2_01_haifa)

relevance.exp.t <- t.test(RELEVANCE_air_obs~RELEVANCE_exp, SVIVA2_01_haifa)

sd.air <- SVIVA2_01_comb%>%summarise(sd(TRUST_air_INDEX,na.rm=T))%>%max()
sd.air.haifa <- SVIVA2_01_comb %>% filter(AREA_center==0)%>%summarise(sd(TRUST_air_INDEX,na.rm=T))%>%max()

sd.waste <- SVIVA2_01_comb%>%summarise(sd(TRUST_waste_INDEX,na.rm=T))%>%max()

```

The outcome variable, citizens' trust in policy, is measured in the survey using a composite index of six items (Cronbach’s alpha = `r alpha_air_trust` for Haifa Bay air-pollution policy, and `r alpha_waste_trust` for recycling policy). This scale was adopted with modifications from validated trust scales used by previous public-administration studies [@karens_2016; @grimmelikhuijsen_2017; @grimmelikhuijsen_2018]. Participants were asked to indicate their agreement with the following statements, between 1 (weakly agree) and 7 (strongly agree): (1) I believe that the actions mentioned in the policy plan will assist in fulfilling the policy goal; (2) I believe that the actions mentioned in the policy plan were designed in a professional manner; (3) I believe that the policy plan is in the interest of citizens; (4) I believe that the policy plan reflects a genuine attempt to improve the well-being of citizens; (5) I believe that EPM made an honest attempt to design a good policy plan; (6) I believe that the Ministry of Environmental Protection aims to keep its commitments in that policy plan. 

The key independent variables are the manipulations of symbols[^footnote_symbols_manipulation_check] and information in the communication, as described above, and their interactions with the perceived personal relevance of the communication. The latter is operationalized observationally (through the distinction between the two areas: Haifa Bay and center) as well as experimentally (through the abovementioned relevance manipulation). 

[^footnote_symbols_manipulation_check]: Manipulation checks for the symbols confirm that the subjects in both areas are fairly familiar with the EPM symbols, and these symbols evoke positive feelings. Analyses for the information manipulation largely confirm that participants perceived both conditions as reliable.  

Living in Haifa Bay area was coded according to respondents’ reporting of residence, work or studying in one of the Haifa Bay cities. To confirm the empirical assumption that the Haifa Bay air-pollution policy (but not the recycling policy) is perceived as more personally relevant by Haifa Bay residents compared with center residents, respondents were asked, after the outcome variable items: "To what extent did you feel that the policy on... is personally relevant to you?" (1="very little"; 7="very much"). `r figure.x=figure.x+1`**Figure `r figure.x`** shows the distributions of the latter items across the two areas. As expected, the Haifa Bay air-pollution policy is perceived as more personally relevant by the local residents compared with those residing in the center (Cohen's D = `r relevance.areas.d[["estimate"]] %>% round(2)` [`r relevance.areas.d[["conf.int"]][["lower"]] %>% round(2)`,`r relevance.areas.d[["conf.int"]][["upper"]] %>% round(2)`]). By comparison, the recycling policy is perceived by both groups as relatively personally relevant by both groups, with no statistically significant differences between them. Additional support for these differences are provided by respondents' answers to the open question presented to the relevance treatment group about the most salient environmental issue in their area of residence. Indeed, `r t1<-SVIVA2_01_haifa %>% filter(RELEVANCE_exp==1,air.content==1) %>% nrow()``r t1` of the `r t2<-SVIVA2_01_haifa %>% filter(RELEVANCE_exp==1) %>% nrow()``r t2` Haifa Bay subjects who were asked this question regarded in their responses to the air-pollution generated from the industrial area (`r air.content.percent.haifa <- t1/t2*100``r air.content.percent.haifa %>% round(0)`%),[^footnote_air_relevance_coding] and many of them explicitly linked it to higher morbidity rates in their area. Subjects from center cities regarded to a variety of issues, including recycling, traffic jams and cleanliness, and only `r ((SVIVA2_01_center %>% filter(RELEVANCE_exp==1,air.content==1) %>% nrow())/nrow(SVIVA2_01_center)*100) %>% round(0)`% of them mentioned air-pollution as a prominent environmental concern in their area.  

```{r}
library(irr)

df.air.content.reliability <- relevance.coding[1:231,] %>% 
  select(air.content,
         air.content.coder2) %>% 
  drop_na(air.content.coder2) %>% 
  as.matrix() %>% 
  t()

```


[^footnote_air_relevance_coding]: I coded any reference to either air-pollution or the chemical factories in the Haifa Bay area, as related to air-pollution. To confirm inter-coder reliability, a second coder independently coded 70 observations (Krippendorff’s alpha = `r kripp.alpha(df.air.content.reliability)[["value"]] %>% round(2)`).  

<br>

[**Figure `r figure.x`** about here]

<br>

As for the relevance manipulation, data suggests that assignment to relevance priming questions did not increase Haifa Bay respondents' perceptions of personal relevance of the air-pollution policy. The Haifa Bay participants assigned to the treatment group reported slightly lower level of personal relevance on average, yet these differences are not statistically significant (Cohen's D = `r relevance.exp.d[["estimate"]] %>% round(2)` [`r relevance.exp.d[["conf.int"]][["lower"]] %>% round(2)`,`r relevance.exp.d[["conf.int"]][["upper"]] %>% round(2)`], *t* = `r relevance.exp.t[["statistic"]] %>% round(3)`, *p*=`r round(relevance.exp.t[["p.value"]],3)`). This limitation should be kept in mind when interpreting the results regarding this manipulation. The results of all manipulation and validation checks are available from the supplementary appendix, along with summary statistics and correlation matrix for all variables (sections 3, 5).

<br>


# Results

I begin by examining the random assignment to the different experimental conditions. `r table.x=table.x+1`**Tables `r table.x`** compares between the different conditions, within each of the two areas, on demographic variables and pre-manipulation covariates. These comparisons confirm that these random assignments yielded fairly balanced groups.[^footnote_balancing_groups_exceptions] The table further suggests that the samples of Haifa Bay and Center residents are fairly balanced with regard to gender, trust in national-level government ministries and interest in environmental issues. Yet, Haifa Bay respondents are somewhat older, more educated, and have more left-wing ideology.

[^footnote_balancing_groups_exceptions]: Within the center respondents, there were statistically significant differences between the symbols groups -- regarding education levels and the percentage of parents; and between the information groups -- regarding the percentage of parents for young children. To adjust for small finite-sample imbalances, as well as for differences between the areas, I also control for covariates in the regression analyses. 

<br>

[**Table `r table.x`** about here]

<br>


```{r}
mod_air_null = lm(TRUST_air_INDEX~1,data=SVIVA2_01_comb)

mod_air_1.1.symbol = update(mod_air_null,. ~ .+
                       SYMBOL_t,
                     data=SVIVA2_01_comb)

mod_air_1.1.info = update(mod_air_null,. ~ .+
                       INFORMATION_weak,
                     data=SVIVA2_01_comb)

mod_air_1.1.symbol.haifa = update(mod_air_null,. ~ .+
                       SYMBOL_t,
                     data=SVIVA2_01_comb %>% filter(AREA_center==0))

mod_air_1.1.symbol.center = update(mod_air_null,. ~ .+
                       SYMBOL_t,
                     data=SVIVA2_01_comb %>% filter(AREA_center==1))


mod_air_1.1.info = update(mod_air_null,. ~ .+
                       INFORMATION_weak,
                     data=SVIVA2_01_comb)
mod_air_1.1.info.haifa = update(mod_air_null,. ~ .+
                       INFORMATION_weak,
                     data=SVIVA2_01_comb%>% filter(AREA_center==0))
mod_air_1.1.info.center = update(mod_air_null,. ~ .+
                       INFORMATION_weak,
                     data=SVIVA2_01_comb%>% filter(AREA_center==1))

main.symbols <- emmeans::emmeans(mod_air_1.1.symbol, specs = pairwise ~ SYMBOL_t)
t.symbols.no <- t.test(TRUST_air_INDEX~SYMBOL_t,data=SVIVA2_01_comb %>% filter(SYMBOL_t!=2))
t.symbols.fake <- t.test(TRUST_air_INDEX~SYMBOL_t,data=SVIVA2_01_comb %>% filter(SYMBOL_t!=0))
t.symbols.nofake <- t.test(TRUST_air_INDEX~SYMBOL_t,data=SVIVA2_01_comb %>% filter(SYMBOL_t!=1))

cond.symbols.haifa <- emmeans::emmeans(mod_air_1.1.symbol.haifa, specs = pairwise ~ SYMBOL_t)
t.symbols.no.haifa <- t.test(TRUST_air_INDEX~SYMBOL_t,data=SVIVA2_01_comb %>% filter(SYMBOL_t!=2,AREA_center==0))
t.symbols.fake.haifa <- t.test(TRUST_air_INDEX~SYMBOL_t,data=SVIVA2_01_comb %>% filter(SYMBOL_t!=0,AREA_center==0))
t.symbols.nofake.haifa <- t.test(TRUST_air_INDEX~SYMBOL_t,data=SVIVA2_01_comb %>% filter(SYMBOL_t!=1,AREA_center==0))




mod_air_1.1.areas = update(mod_air_null,. ~ .+
                       AREA_center,
                     data=SVIVA2_01_comb)

main.info <- mod_air_1.1.info %>% tidy()
t.info <- t.test(TRUST_air_INDEX~INFORMATION_weak,data=SVIVA2_01_comb)

main.areas <- mod_air_1.1.areas %>% tidy()
t.areas <- t.test(TRUST_air_INDEX~AREA_center,data=SVIVA2_01_comb)

```

I now turn to exploring the main outcome variable of interest -- respondents trust in the *Haifa Bay air-pollution policy*. This outcome variable has a right-skewed distribution (*Mean* = `r SVIVA2_01_comb%>%summarise(mean(TRUST_air_INDEX,na.rm=T))%>%max() %>% round(2)`, *SD* = `r sd.air %>% round(2)`, *Median* = `r SVIVA2_01_comb%>%summarise(median(TRUST_air_INDEX,na.rm=T))%>%max() %>% round(2)`), which entails that more subjects reported very low levels of trust, than very high levels of trust in than policy. `r figure.x=figure.x+1`**Figure `r figure.x`** shows the means and distributions of the outcome variable across the different experimental conditions of symbolic elements and information, and across the two areas. The real symbols condition (in the left panel) has a positive and significant main effect on trust in that policy plan compared with the no symbols condition (*diff* = `r (main.symbols$contrasts %>% tidy() %>% .[1,"estimate"]*-1) %>% max(.) %>% round(.,2)` [`r (t.symbols.no[["conf.int"]][2]*-1) %>% round(.,2)`,`r (t.symbols.no[["conf.int"]][1]*-1) %>% round(.,2)`], *t* = `r t.symbols.no[["statistic"]][["t"]] %>% abs() %>% round(.,3)`). That effect amounts to `r (main.symbols$contrasts %>% tidy() %>% .[1,"estimate"] %>% max(.)/sd.air) %>% abs() %>% round(2)` SDs [`r (t.symbols.no[["conf.int"]][2]/sd.air) %>% abs() %>% round(2)`,`r (t.symbols.no[["conf.int"]][1]/sd.air) %>% abs() %>% round(2)`]. Subjects who saw the real symbols also reported, on average, greater trust in the policy, compared with those who received fake symbols, yet these differences are not significant at the 95% level (*diff* = `r (main.symbols$contrasts %>% tidy() %>% .[3,"estimate"]) %>% max(.) %>% round(.,2)` [`r (t.symbols.fake[["conf.int"]][1]) %>% round(.,2)`,`r (t.symbols.fake[["conf.int"]][2]) %>% round(.,2)`], *t* = `r t.symbols.fake[["statistic"]][["t"]] %>% abs() %>% round(.,3)`, *p* = `r (t.symbols.fake[["p.value"]]) %>% abs() %>% round(.,3)`). The increase in the lower bound of the box in the real symbols shows that real symbols reduced to a great extent the number of participants who reported very low trust score. Descriptively, `r ((filter(SVIVA2_01,SYMBOL==0,TRUST_air_INDEX<=2) %>% nrow())/(filter(SVIVA2_01,SYMBOL==0) %>% nrow()) *100) %>% round(1)`% of the participants in the no symbols group reported a score of 2 or less which indicates very low level of trust, compares with `r ((filter(SVIVA2_01,SYMBOL==1,TRUST_air_INDEX<=2) %>% nrow())/(filter(SVIVA2_01,SYMBOL==1) %>% nrow()) *100) %>% round(1)`% and only `r ((filter(SVIVA2_01,SYMBOL==2,TRUST_air_INDEX<=2) %>% nrow())/(filter(SVIVA2_01,SYMBOL==2) %>% nrow()) *100) %>% round(1)`% in the real symbols group. As shown in the middle panel, the main effect of the weak information condition is negative and significant (*diff* = `r (main.info %>% .[2,"estimate"]*-1) %>% max(.) %>% round(.,2)` [`r (t.info[["conf.int"]][2]) %>% round(.,2)`-`r (t.info[["conf.int"]][1]) %>% round(.,2)`], *t* = `r t.info[["statistic"]][["t"]] %>% abs() %>% round(.,3)`). That effect amounts to `r ((main.info %>% .[2,"estimate"]*-1)/sd.air) %>% max(.) %>% round(.,2)` SDs [`r (t.info[["conf.int"]][1]/sd.air) %>% abs() %>% round(2)`,`r (t.info[["conf.int"]][2]/sd.air) %>% abs() %>% round(2)`]. Finally, as presented in the right panel, participants from Haifa Bay reported significantly lower levels of trust in the Haifa Bay air-pollution policy, compared to center participants (*diff* = `r (main.areas %>% .[2,"estimate"]*-1) %>% max(.) %>% abs() %>% round(.,2)` [`r (t.areas[["conf.int"]][2]) %>% round(.,2) %>% abs()`,`r (t.areas[["conf.int"]][1]) %>% round(.,2) %>% abs()`], *t* = `r t.areas[["statistic"]][["t"]] %>% abs() %>% round(.,3)`). Descriptively, `r ((filter(SVIVA2_01_haifa,TRUST_air_INDEX<4) %>% nrow())/(nrow(SVIVA2_01_haifa))*100)%>%round(1)`% of Haifa Bay subjects have a trust-index score lower than 4, the scale's middle point (compared with `r ((filter(SVIVA2_01_center,TRUST_air_INDEX<4) %>% nrow())/(nrow(SVIVA2_01_center))*100)%>% round(1)`% of the center subjects). This indicates that the majority of the sampled Haifa Bay residents tend to have prior negative attitudes towards EPM's actions regarding the air-pollution in their area.    

<br>

[**Figure `r figure.x`** about here]

<br>

Next, I examine the interactions between the symbols and information manipulation, and the two areas. To reiterate, based on my theoretical hypotheses, I expect that subjects living in the Haifa Bay, who perceive this issue as personally relevant, would be less affected by the symbolic elements, compared with those living in the Center (H<sub>1</sub>). Likewise, I expect Haifa Bay respondents to be more affected by the differences in the argument quality of the communication (strong versus weak information), compared with center respondents (H<sub>2</sub>). `r figure.x=figure.x+1`**Figure `r figure.x`** graphically illustrates the conditional effects of the manipulations, within each of the two areas.[^footnote_raw_means] As for the effect of the symbols manipulation (displayed in the left panel), the results show that the symbols had a greater positive effect on those living in the polluted Haifa Bay area. Their exposure to the real symbols increased their trust in the policy by `r (cond.symbols.haifa$contrasts %>% tidy() %>% .[1,"estimate"] %>% max(.)/sd.air) %>% abs() %>% round(2)` SDs [`r (t.symbols.no.haifa[["conf.int"]][2]/sd.air) %>% abs() %>% round(2)`,`r (t.symbols.no.haifa[["conf.int"]][1]/sd.air) %>% abs() %>% round(2)`] compared with the no symbols group, and `r (cond.symbols.haifa$contrasts %>% tidy() %>% .[3,"estimate"] %>% max(.)/sd.air) %>% abs() %>% round(2)` SDs [`r (t.symbols.fake.haifa[["conf.int"]][1]/sd.air) %>% abs() %>% round(2)`,`r (t.symbols.fake.haifa[["conf.int"]][2]/sd.air) %>% abs() %>% round(2)`] compared to the fake symbols group. The center participants who saw the real symbols also reported, on average, higher levels of trust in the policy, compared with the two control groups. However, these differences were smaller compared with the effect among the Haifa Bay subjects, and not sufficiently significant. The effects of the information manipulation on the two groups are presented in the right panel. This comparison shows that the differences between participants' trust in the weak and strong policies were roughly similar in both groups.  

[^footnote_raw_means]: Means and SDs of outcome variables across the two areas and the experimental conditions are reported in the supplementary appendix (section 6).  

<br>

[**Figure `r figure.x`** about here]

<br>

These results are at odds with the initial theoretical expectations. They suggest that that those residing in the polluted area were equally, and perhaps even more, likely to be influenced by symbolic elements in the communication (and to a lesser extent, by the unfamiliar graphical elements in the fake symbols). Similarly, these analyses do not provide evidence that Haifa Bay residents were more likely than others to scrutinize the communication, and differentiate between strong and weak arguments.

```{r}

mod_air_1.1 = update(mod_air_null,. ~ .+
                       SYMBOL_t+
                       INFORMATION_weak,
                     data=SVIVA2_01_comb)
mod_air_1.2 = update(mod_air_1.1,. ~ .+AREA_center)
mod_air_1.3 = update(mod_air_1.1,. ~ .+AREA_center*(SYMBOL_t+INFORMATION_weak))

mod_air_1.4 = update(mod_air_1.3,. ~ .+
                      GENDER+
                       AGE+
                       GOV_TRUST+
                       IDEOLOGY+
                       EDUCATION+
                       INCOME+
                       CHILDREN_young)

mod_air_1.5 = update(mod_air_1.3,. ~ .,data=SVIVA2_01_comb_air_first)


## Adjust standard errors & F statistic
mod_air_1.1.robust_se    <- sqrt(diag(vcovHC(mod_air_1.1, type = "HC1")))
mod_air_1.1.wald_results <- waldtest(mod_air_1.1, vcov = vcovHC(mod_air_1.1, type = "HC1"))

mod_air_1.2.robust_se    <- sqrt(diag(vcovHC(mod_air_1.2, type = "HC1")))
mod_air_1.2.wald_results <- waldtest(mod_air_1.2, vcov = vcovHC(mod_air_1.2, type = "HC1"))

mod_air_1.3.robust_se    <- sqrt(diag(vcovHC(mod_air_1.3, type = "HC1")))
mod_air_1.3.wald_results <- waldtest(mod_air_1.3, vcov = vcovHC(mod_air_1.3, type = "HC1"))

mod_air_1.4.robust_se    <- sqrt(diag(vcovHC(mod_air_1.4, type = "HC1")))
mod_air_1.4.wald_results <- waldtest(mod_air_1.4, vcov = vcovHC(mod_air_1.4, type = "HC1"))

mod_air_1.5.robust_se    <- sqrt(diag(vcovHC(mod_air_1.5, type = "HC1")))
mod_air_1.5.wald_results <- waldtest(mod_air_1.5, vcov = vcovHC(mod_air_1.5, type = "HC1"))

```

I further examined the interactions between the manipulations and the two areas via a series of multiple linear regression models, presented in `r table.x=table.x+1`**Table `r table.x`**. In Model 1.1 I regress trust in Haifa Bay air-pollution policy on the manipulations, then I add the areas and the interaction between them (Models 1.2, 1.3). In Model 1.4, I test the robustness of the interaction model by controlling for individual-level covariates.[^footnote_control_variables] In all regression models, I use robust standard errors to adjust for heteroskedacity.[^footnote_heteroskedacity] The results of these models are largely consistent with the raw findings presented above. In Models 1.3, 1.4 the interactions between living in the center and the real symbols are negative and significant at the 90% level, and the interactions with the weak information are insignificant. A comparison between models 1.2 and 1.3 via ANOVA reveals that adding these interactions do not significantly improve the model fit (*F(`r anova(mod_air_1.2,mod_air_1.3) %>% tidy() %>% select(df) %>% .[2,] %>% max()`*) = `r anova(mod_air_1.2,mod_air_1.3) %>% tidy() %>% select(statistic) %>% .[2,] %>% max() %>% round(2)`, *p*= `r anova(mod_air_1.2,mod_air_1.3) %>% tidy() %>% select(p.value) %>% .[2,] %>% max() %>% round(2)`). 

[^footnote_heteroskedacity]: Breusch-Pagan test confirms the presence of heteroskedasticity (for Model 1.3, Breusch-Pagan test =  `r bptest(mod_air_1.3)[["statistic"]][["BP"]] %>% round(3)`, *p*=`r bptest(mod_air_1.3)[["p.value"]][["BP"]] %>% round(3)`). Robust standard errors are estimated using R sandwich package with estimation type "HC1".    

[^footnote_control_variables]: I control for all the variables that are included in the balancing tables, except for interest in environmental issues, which was reported by only half of the sample. In other analyses, I also included that variable, with no significant change to the main results. 

<br>

[**Table `r table.x`** about here]

<br>


```{r}

mod_waste_null = lm(TRUST_waste_INDEX~1,data=SVIVA2_01_comb)
mod_waste_1.1 = update(mod_waste_null,. ~ .+
                       SYMBOL_t+
                       INFORMATION_weak,
                     data=SVIVA2_01_comb)
mod_waste_1.2 = update(mod_waste_1.1,. ~ .+AREA_center)
mod_waste_1.3 = update(mod_waste_1.1,. ~ .+AREA_center*(SYMBOL_t+INFORMATION_weak))
mod_waste_1.4 = update(mod_waste_1.3,. ~ .+
                      GENDER+
                       AGE+
                       GOV_TRUST+
                       IDEOLOGY+
                       EDUCATION+
                       INCOME+
                       CHILDREN_young)
SVIVA2_01_comb_recycling_first <- SVIVA2_01_comb %>% filter(AIR_order==2)
mod_waste_1.5 = update(mod_waste_1.3,. ~ .,data=SVIVA2_01_comb_recycling_first)

## Adjust standard errors & F statistic
mod_waste_1.1.robust_se    <- sqrt(diag(vcovHC(mod_waste_1.1, type = "HC1")))
mod_waste_1.1.wald_results <- waldtest(mod_waste_1.1, vcov = vcovHC(mod_waste_1.1, type = "HC1"))

mod_waste_1.2.robust_se    <- sqrt(diag(vcovHC(mod_waste_1.2, type = "HC1")))
mod_waste_1.2.wald_results <- waldtest(mod_waste_1.2, vcov = vcovHC(mod_waste_1.2, type = "HC1"))

mod_waste_1.3.robust_se    <- sqrt(diag(vcovHC(mod_waste_1.3, type = "HC1")))
mod_waste_1.3.wald_results <- waldtest(mod_waste_1.3, vcov = vcovHC(mod_waste_1.3, type = "HC1"))

mod_waste_1.4.robust_se    <- sqrt(diag(vcovHC(mod_waste_1.4, type = "HC1")))
mod_waste_1.4.wald_results <- waldtest(mod_waste_1.4, vcov = vcovHC(mod_waste_1.4, type = "HC1"))

mod_waste_1.5.robust_se    <- sqrt(diag(vcovHC(mod_waste_1.5, type = "HC1")))
mod_waste_1.5.wald_results <- waldtest(mod_waste_1.5, vcov = vcovHC(mod_waste_1.4, type = "HC1"))


SVIVA2_01_comb <- SVIVA2_01_comb %>% 
  mutate(SYMBOL_t.r = Recode(SYMBOL_t,"0=2;2=0"))
  
mod_waste_1.1.r = update(mod_waste_null,. ~ .+
                       SYMBOL_t.r+
                       INFORMATION_weak)
mod_waste_1.1.r.robust_se <- sqrt(diag(vcovHC(mod_waste_1.1.r, type = "HC1")))

mod_waste_1.3.r = update(mod_waste_null,. ~ .+
                       AREA_center*(SYMBOL_t.r+INFORMATION_weak))
mod_waste_1.3.r.robust_se <- sqrt(diag(vcovHC(mod_waste_1.3.r, type = "HC1")))
```


In summary, the findings with regard to the Haifa Bay air-pollution policy indicate, tentatively, that higher levels of perceived personal relevance of the communication neither attenuate the positive effect of brand elements entangled in communications, nor enhances citizens' attention to their substantive content. As discussed in the methodology section, this analysis is based on an observational comparison, and hence a concern of endogeneity cannot be entirely ruled. Accordingly, the null findings regarding the interactions between the areas and the manipulations can be resulted by other, unobserved differences between the two populations. Yet, as explained, the experimental design provides two additional tests which enable to address this concern: the comparison to the recycling policy, for which there are no relevance differences between the two areas, and the relevance manipulation. I now discuss the results of these two tests.

`r table.x=table.x+1`**Table `r table.x`** replicates the above models for trust in the recycling policy (*Mean* = `r SVIVA2_01_comb%>%summarise(mean(TRUST_waste_INDEX,na.rm=T))%>%max() %>% round(2)`, *SD* = `r SVIVA2_01_comb%>%summarise(sd(TRUST_waste_INDEX,na.rm=T))%>%max() %>% round(2)`, *Median* = `r SVIVA2_01_comb%>%summarise(median(TRUST_waste_INDEX,na.rm=T))%>%max() %>% round(2)`). Overall, these models reveal relatively similar patterns to those found in the air-pollution policy. Notice that Haifa Bay subjects reported lower trust on that policy as well, which may indicate that they their negative perceptions about the Ministry's actions regarding the air-pollution in their area shapes their general perceptions of the Ministry. The directions of the simple effects and interaction terms remain intact, while the main effects and conditional effects are smaller and less significant.[^footnote_recycling_model] Consistently with the air-pollution policy, Haifa Bay subjects were more affected by the symbols, and the two areas responded similarly to the differences in the information. The fact that similar patterns are observed across the these policies further weakens the hypotheses regarding the moderating role of personal relevance. It also rules out a possible argument that greater relevance enhances (rather than reduces) the effect of symbols. If this was the case, then we should not have found a similar interaction in relation to the recycling policy.    

```{r}

symbols.waste <- emmeans::emmeans(mod_waste_1.1, specs = pairwise ~ SYMBOL_t)

diff1 <- (symbols.waste$contrasts %>% tidy() %>% .[1,"estimate"] %>% max(.)) %>% abs() %>% round(3)
se1 <- (mod_waste_1.1.robust_se %>% tidy() %>% filter(names=="SYMBOL_t1") %>% select(x)) %>% max()

diff2 <- (symbols.waste$contrasts %>% tidy() %>% .[3,"estimate"] %>% max(.)) %>% abs() %>% round(3)
se2 <- (mod_waste_1.1.r.robust_se %>% tidy() %>% filter(names=="SYMBOL_t.r1") %>% select(x)) %>% max()

diff3 <- (mod_waste_1.3 %>% tidy() %>% filter(term=="SYMBOL_t1") %>% select(estimate) %>% max(.)) %>% abs() %>% round(3)
se3 <- (mod_waste_1.3.robust_se %>% tidy() %>% filter(names=="SYMBOL_t1") %>% select(x)) %>% max()

diff4 <- (mod_waste_1.3.r %>% tidy() %>% filter(term=="SYMBOL_t.r1") %>% select(estimate) %>% max(.)) %>% abs() %>% round(3)
se4 <- (mod_waste_1.3.r.robust_se %>% tidy() %>% filter(names=="SYMBOL_t.r1") %>% select(x)) %>% max()

diff5 <- (mod_waste_1.1 %>% tidy() %>% filter(term=="INFORMATION_weak") %>% select(estimate) %>% max(.)) %>% abs() %>% round(3)
se5 <- (mod_waste_1.1.robust_se %>% tidy() %>% filter(names=="INFORMATION_weak") %>% select(x)) %>% max()
```

[^footnote_recycling_model]: The main effect of the real symbols (as depicted from Model 2.1) is significant compared with the no symbols group (*Diff*=`r diff1` [`r (diff1-1.96*se1) %>% round(3)`-`r (diff1+1.96*se1) %>% round(3)`], *t*=`r (diff1/se1) %>% round(3)`). Yet, the increase is not significant not with the fake symbols group (*Diff*=`r diff2` [`r (diff2-1.96*se2) %>% round(3)`-`r (diff2+1.96*se2) %>% round(3)`], *t*=`r (diff2/se2) %>% round(3)`). As shown in Model 2.3, the real symbols significantly increased Haifa Bay subjects' trust in that policy compared with the no symbols group (*Diff*=`r diff3` [`r (diff3-1.96*se3) %>% round(3)`-`r (diff3+1.96*se3) %>% round(3)`], *t*=`r (diff3/se3) %>% round(3)`), whereas the increase compared with the fake symbols group was not sufficiently significant (*Diff*=`r diff4` [`r (diff4-1.96*se4) %>% round(3)`-`r (diff4+1.96*se4) %>% round(3)`], *t*=`r (diff4/se4) %>% round(3)`). The differences between the symbols groups among the center subjects are small and insignificant. The weaker effect of the real symbols can be partly explained by the fact that I used a different celebrity campaign image for this policy, which was less familiar compared with the image presented for the Haifa Bay air-pollution policy. The main effect of weak information (as depicted from Model 2.1) amounts to `r (diff5/sd.waste) %>% round(3)` SDs [`r ((diff5-1.96*se5)/sd.waste) %>% round(3)`-`r ((diff5+1.96*se5)/sd.waste) %>% round(3)`]. 

<br>

[**Table `r table.x`** about here]

<br>

```{r}

mod_relevance_null = lm(TRUST_air_INDEX~1,data=SVIVA2_01_comb.haifa)
mod_relevance_1.1 = update(mod_relevance_null,. ~ .+
                             SYMBOL_t+
                   INFORMATION_weak+
                   RELEVANCE_exp)
mod_relevance_1.2 = update(mod_relevance_1.1,. ~ .+RELEVANCE_exp*(SYMBOL_t+INFORMATION_weak))
mod_relevance_1.3 = update(mod_relevance_1.2,. ~ .+
                   GENDER+
                   AGE+
                   GOV_TRUST+
                   IDEOLOGY+
                   EDUCATION+
                   INCOME+
                   CHILDREN_young)


mod_relevance_1.4 = update(mod_relevance_1.2,. ~ .,data=SVIVA2_01_comb.haifa_air_first)

## Adjust standard errors & F statistic
mod_relevance_1.1.robust_se    <- sqrt(diag(vcovHC(mod_relevance_1.1, type = "HC1")))
mod_relevance_1.1.wald_results <- waldtest(mod_relevance_1.1, vcov = vcovHC(mod_relevance_1.1, type = "HC1"))

mod_relevance_1.2.robust_se    <- sqrt(diag(vcovHC(mod_relevance_1.2, type = "HC1")))
mod_relevance_1.2.wald_results <- waldtest(mod_relevance_1.2, vcov = vcovHC(mod_relevance_1.2, type = "HC1"))

mod_relevance_1.3.robust_se    <- sqrt(diag(vcovHC(mod_relevance_1.3, type = "HC1")))
mod_relevance_1.3.wald_results <- waldtest(mod_relevance_1.3, vcov = vcovHC(mod_relevance_1.3, type = "HC1"))

mod_relevance_1.4.robust_se    <- sqrt(diag(vcovHC(mod_relevance_1.4, type = "HC1")))
mod_relevance_1.4.wald_results <- waldtest(mod_relevance_1.4, vcov = vcovHC(mod_relevance_1.3, type = "HC1"))


SVIVA2_01_comb.haifa <- SVIVA2_01_comb.haifa %>% 
  mutate(RELEVANCE_exp.r = Recode(RELEVANCE_exp,"0=1;1=0"),
         SYMBOL_t.r = Recode(SYMBOL_t,"0=2;2=0"))

mod_relevance_1.2.r = update(mod_relevance_null,. ~ .+RELEVANCE_exp.r*(SYMBOL_t+INFORMATION_weak))

mod_relevance_1.2.r.robust_se <- sqrt(diag(vcovHC(mod_relevance_1.2.r, type = "HC1")))

diff5 <- (mod_relevance_1.2.r %>% tidy() %>% filter(term=="SYMBOL_t1") %>% select(estimate) %>% max(.)) %>% abs() %>% round(3)
se5 <- (mod_relevance_1.2.r.robust_se %>% tidy() %>% filter(names=="SYMBOL_t1") %>% select(x)) %>% max()


mod_relevance_1.2.r.r = update(mod_relevance_null,. ~ .+RELEVANCE_exp.r*(SYMBOL_t.r+INFORMATION_weak))

tab.t1 <- SVIVA2_01_comb.haifa %>% 
  group_by(RELEVANCE_exp,
           INFORMATION_weak) %>% 
  summarise(n = n(),
            mean = mean(TRUST_air_INDEX,na.rm = T),
            sd = sd(TRUST_air_INDEX,na.rm = T),
            q1 = quantile(TRUST_air_INDEX,0.25,na.rm=T),
            median = quantile(TRUST_air_INDEX,0.5,na.rm=T),
            q3 = quantile(TRUST_air_INDEX,0.75,na.rm=T)) %>% 
  mutate(se = sd/sqrt(n)) %>% 
  mutate(ci.low = mean - 1.96*se,
         ci.high = mean + 1.96*se)

mod_symbols_relevance_aircontent = lm(TRUST_air_INDEX ~ SYMBOL_t,SVIVA2_01_haifa.relevance.aircontent)

mod_symbols_relevance_aircontent.r = lm(TRUST_air_INDEX ~ SYMBOL_t.r,SVIVA2_01_haifa.relevance.aircontent)

main.symbols.relevance.aircontent <- emmeans::emmeans(mod_symbols_relevance_aircontent, specs = pairwise ~ SYMBOL_t)

t.symbols.relevance.aircontent.no <- t.test(TRUST_air_INDEX~SYMBOL_t,data=SVIVA2_01_haifa.relevance.aircontent %>% filter(SYMBOL_t!=2))
t.symbols.relevance.aircontent.fake <- t.test(TRUST_air_INDEX~SYMBOL_t,data=SVIVA2_01_haifa.relevance.aircontent %>% filter(SYMBOL_t!=0))

```

Next, `r table.x=table.x+1`**Table `r table.x`** presents the interaction between the relevance manipulation and the symbols and information manipulations. In Model 3.1, I regress trust in Haifa Bay air-pollution policy on the three manipulations, then in Model 3.2 I add the interaction between the relevance manipulation and the symbols and information manipulations, and in Model 3.3 I add controls. In all these models, the coefficient of the relevance treatment is negative but far from significance. The interactions between the real symbols and the relevance manipulation are negative, which indicates that among those who were primed to think about the personal relevance of the air-pollution problem, the increase in trust due to the appearance of the familiar symbols was slightly smaller. However, these interaction coefficients are far from significance and relatively weak (estimates range between `r ((mod_relevance_1.2 %>% tidy() %>% filter(term=="SYMBOL_t1:RELEVANCE_exp") %>% select(estimate) %>% max(.) %>% abs())/sd.air.haifa) %>% round(2)`--`r ((mod_relevance_1.3 %>% tidy() %>% filter(term=="SYMBOL_t1:RELEVANCE_exp") %>% select(estimate) %>% max(.) %>% abs())/sd.air.haifa) %>% round(2)` SDs, and attenuating approximately `r ((mod_relevance_1.2 %>% tidy() %>% filter(term=="SYMBOL_t1:RELEVANCE_exp") %>% select(estimate) %>% max(.) %>% abs())/(mod_relevance_1.2 %>% tidy() %>% filter(term=="SYMBOL_t1") %>% select(estimate) %>% max(.) %>% abs())*100) %>% round(0)`--`r ((mod_relevance_1.3 %>% tidy() %>% filter(term=="SYMBOL_t1:RELEVANCE_exp") %>% select(estimate) %>% max(.) %>% abs())/(mod_relevance_1.3 %>% tidy() %>% filter(term=="SYMBOL_t1") %>% select(estimate) %>% max(.) %>% abs())*100) %>% round(0)`% of the effect in the control group). The interactions between the relevance treatment and weak information manipulations are also in the expected direction, but again insignificant (estimates range between `r ((mod_relevance_1.3 %>% tidy() %>% filter(term=="INFORMATION_weak:RELEVANCE_exp") %>% select(estimate) %>% max(.) %>% abs())/sd.air.haifa) %>% round(2)`--`r ((mod_relevance_1.2 %>% tidy() %>% filter(term=="INFORMATION_weak:RELEVANCE_exp") %>% select(estimate) %>% max(.) %>% abs())/sd.air.haifa) %>% round(2)` SDs, and attenuating approximately `r ((mod_relevance_1.3 %>% tidy() %>% filter(term=="INFORMATION_weak:RELEVANCE_exp") %>% select(estimate) %>% max(.) %>% abs())/(mod_relevance_1.3 %>% tidy() %>% filter(term=="INFORMATION_weak") %>% select(estimate) %>% max(.) %>% abs())*100) %>% round(0)`--`r ((mod_relevance_1.2 %>% tidy() %>% filter(term=="INFORMATION_weak:RELEVANCE_exp") %>% select(estimate) %>% max(.) %>% abs())/(mod_relevance_1.2 %>% tidy() %>% filter(term=="INFORMATION_weak") %>% select(estimate) %>% max(.) %>% abs())*100) %>% round(0)`% of the effect in the control group).[^footnote_relevance_manipulation_1] Hence, these analyses also do not lend sufficient support for the hypotheses.   

[^footnote_relevance_manipulation_1]: In additional analyses, I also examine the moderating effect of the relevance treatment for center subjects' responses to Haifa Bay air-pollution policy, as well as for the recycling policy (for the entire sample). In all these analyses, the interactions with the relevance treatment are insignificant.  

Two limitations about these latter analyses should be acknowledged. First, since the treatment manipulation did not affect participants' self-reporting of the personal relevance, there is no certainty that the manipulation actually succeeded in modifying the perceptions of those subjects. Second, these analyses have more limited statistical power, compared to the observational comparisons, since they are restricted only to the sample of Haifa Bay subjects. Still, based on the interactions in Model 3.2, we can assess that even if the relevance manipulation does attenuate the effect of the real symbols, their effect on that group compared with the no symbols can be estimated as `r (diff5/sd.air.haifa) %>% round(3)` SDs [`r ((diff5 - 1.96*se5)/sd.air.haifa) %>% round(3)`,`r ((diff5 + 1.96*se5)/sd.air.haifa) %>% round(3)`]. Remarkably, even when restricting the relevance treatment group to those who mentioned the air-pollution as an acute environmental issue in their area of residence (`r air.content.percent.haifa %>% round(0)`% of that group, *n* = `r SVIVA2_01_haifa.relevance.aircontent %>% nrow()`), the real symbols still had a roughly similar positive effect on trust in the policy plan, compared with the no symbols (*diff* = `r (main.symbols.relevance.aircontent$contrasts %>% tidy() %>% .[1,"estimate"]*-1) %>% max(.) %>% round(.,2)` [`r (t.symbols.relevance.aircontent.no[["conf.int"]][2]*-1) %>% round(.,2)`-`r (t.symbols.relevance.aircontent.no[["conf.int"]][1]*-1) %>% round(.,2)`], *t* = `r t.symbols.relevance.aircontent.no[["statistic"]][["t"]] %>% abs() %>% round(.,3)`).[^footnote_relevance_significance_fakesymbols] Overall, the data indicates that even Haifa Bay residents that were reminded about the relevance of the air-pollution to their lives area, were positively affected by familiar symbols of the ministry, at least to some degree.   

[^footnote_relevance_significance_fakesymbols]: Consistently with the previous models, subjects in the real symbols group also reported higher trust compared with the fake symbols, but these differences are not sufficiently significant. The differences between the two control groups are insignificant. 

Finally, I further explored the robustness of these findings through additional analyses, reported in the supplementary appendix (section 7). *First*, I replicate the main models while restricting the analyses to the first policy seen by the participants. This enables me to rule out the possibility that subjects' responses are affected by the manipulations in the first policy presented to them (a carryover effect), or by their evaluation of the it (assimilation or contrast effects). *Second*, I restrict the analyses to Haifa Bay residents, and compare between those who are more/less likely to perceive the air-pollution as personally relevant, based on two proxies: (a) Subjects city of residence, and its geographical proximity to the polluting industrial area; (b) parents for young children. *Third*, I replicated the analyses on the unfiltered sample. All these additional analyses are largely consistent with the analyses presented in the paper, and do not provide evidence that greater levels of perceived personal relevance attenuates the effect of symbols.       

<br>

# Discussion and conclusion 

This study contributes to the growing public-administration research on government branding and its potency to influence and possibly manipulate citizens' perceptions of government organizations and their policies and services. I explored whether persuasion through branding and symbolic communication can also occur in policy matters that have significant, clear implications for citizens' well-being. I examined the hypothesis, derived from social psychology theory, that persuasion via symbols is attenuated the more citizens perceive the communication as personally relevant. I put this hypothesis to rigorous empirical testing using a randomized survey experiment focused on air-pollution policy, utilizing a natural variation in the perceived personal relevance between citizens residing in a polluted area and others, residing elsewhere. To the best of my knowledge, this is the first study to explore, theoretically and empirically, the moderating role of perceived personal relevance on citizens' responses to government branding, and to government public communications at large.

Contrary to my expectations, and those of extant research in social psychology and marketing, I did not find that higher levels of perceived personal relevance attenuate the effect of symbols. While subjects residing in the polluted Haifa Bay reported in the survey that they perceive the governmental policy for reducing air-pollution in that area as highly relevant for their lives, they were similarly, and perhaps even more affected by the appearance of familiar brand elements of the Environmental Protection Ministry in the communication of that policy plan. Congruently, they did not pay more attention to the substantive content of the policy plan. Furthermore, I find that even priming Haifa Bay residents to think about the personal relevance of the air-pollution did not significantly attenuate their susceptibility to persuasion by the symbols. 

How can we reconcile the findings of this study with the proposed theory? One plausible explanation is that despite the relatively high involvement of Haifa Bay residents, they were still not motivated enough to rely only on the central route and disregard the peripheral cues. If this explanation holds, it entails that citizens may resist the emotional effect of symbols perhaps only in rare circumstances, which are characterized by extremely high levels of personal relevance -- policies that have immediate, tangible and unambiguous consequences for citizens well-being. Indeed, social psychology ELM scholars Petty and Cacioppo have acknowledged that the high and low personal relevance conditions generated in their experimental settings were deliberately created for theory testing purposes, and can be rarely found in the world outside the laboratory [@petty_1986, p. 206]. 

How can we explain the finding that the positive effect of the real symbols was stronger to some extent among Haifa Bay residents? Given that this pattern is also evident for the recycling policy, it is unlikely that it is linked to differences in perceived personal relevance. Rather, I tentatively suggest two other possible explanations for that. *First*, these patterns could be the result of the higher intercept of center residents' trust in EPM policies, or a ceiling effect. More generally, it could be the case that symbolic elements are more effective in shifting people from low to medium levels of trust (i.e. as in the case of Haifa Bay subjects), than shifting them from medium to high levels (i.e. as in the case of center subjects, and the recycling policy). In other words, symbols may be more effective in reducing citizens' distrust, then increasing their trust.[^footnote_ceiling_effect] A *second* explanation could be that EPM symbols attenuated the effect of Haifa Bay residents' negative prior beliefs on their judgments of the policy plans. Accordingly, there could be an interaction between the emotive effect of symbols and citizens' prior positions embedded in their group affiliation and social identities (in our case, the local identity of Haifa Bay residents). I theorize, tentatively, that the direction of the interaction depends on whether or not the symbolic associations are related to the relevant group identities. When the symbols are not strongly linked to identities of specific groups having strong prior positions on the policy or the organization, as in this case, symbols would *attenuate* the influence of the prior beliefs on people's judgements. Conversely, when symbolic associations activate group identities and make them more salient, they may *enhance* the effect of dominant positions embedded in them, and increase polarization between parties [@teodoro_2018]. An interaction between symbols and citizens' prior beliefs may also suggest that symbols can affect citizens' susceptibility to motivated reasoning and biased processing of new information [@taber_2006; @christensen_2018; @baekgaard_2016; @james_2017a]. Hence, future research may further examine the interactions between different types of symbols and citizens' prior beliefs and motivated reasoning. 

[^footnote_ceiling_effect]: This explanation is supported by the comparison of the percentages of subjects who reported very low levels of trust in the two policies. In the Haifa Bay, the percentage of those who reported a trust score of less than 2 in the Haifa Bay air-pollution policy decreased from `r ((filter(SVIVA2_01_haifa,SYMBOL==0,TRUST_air_INDEX<2) %>% nrow())/(filter(SVIVA2_01_haifa,SYMBOL==0)%>%nrow())*100)%>%round(1)`% in the no symbols group to `r ((filter(SVIVA2_01_haifa,SYMBOL==2,TRUST_air_INDEX<2) %>% nrow())/(filter(SVIVA2_01_haifa,SYMBOL==2)%>%nrow())*100)%>%round(1)`% in the real symbols. Among the center subjects, only `r ((filter(SVIVA2_01_center,SYMBOL==0,TRUST_air_INDEX<2) %>% nrow())/(filter(SVIVA2_01_center,SYMBOL==0)%>%nrow())*100)%>%round(1)`% of the no symbols group reported a trust score lower than 2 in the first place, and this percentage dropped to `r ((filter(SVIVA2_01_center,SYMBOL==2,TRUST_air_INDEX<2) %>% nrow())/(filter(SVIVA2_01_center,SYMBOL==2)%>%nrow())*100)%>%round(1)`% among the real symbols group. With regard to the recycling policy, the percentage decreased from `r ((filter(SVIVA2_01_haifa,SYMBOL==0,TRUST_waste_INDEX<2) %>% nrow())/(filter(SVIVA2_01_haifa,SYMBOL==0)%>%nrow())*100)%>%round(1)`% to `r ((filter(SVIVA2_01_haifa,SYMBOL==2,TRUST_waste_INDEX<2) %>% nrow())/(filter(SVIVA2_01_haifa,SYMBOL==2)%>%nrow())*100)%>%round(1)`% in the Haifa Bay and from `r ((filter(SVIVA2_01_center,SYMBOL==0,TRUST_waste_INDEX<2) %>% nrow())/(filter(SVIVA2_01_center,SYMBOL==0)%>%nrow())*100)%>%round(1)`% to `r ((filter(SVIVA2_01_center,SYMBOL==2,TRUST_waste_INDEX<2) %>% nrow())/(filter(SVIVA2_01_center,SYMBOL==2)%>%nrow())*100)%>%round(1)`% in the center. 

Still, several limitations of this study should be acknowledged. The study focuses on one policy domain -- environmental policy, and on a specific issue -- air-pollution in a specific area. Future studies may put these findings to further test by exploring cases of air-pollution in other locations, as well as by exploring cases of other environmental policies, and other domains. Additionally, despite the multiple comparisons presented, I still cannot entirely rule out the possibility that the differences in personal relevance between the two geographic areas are confounded with other differences associated with people's beliefs about the subject and the agency. Future studies may conduct more rigorous investigations through natural experimental or quasi-experimental designs that could take advantage of particular cases of citizens' random allocation to varying levels of personal relevance. Finally, whereas this study includes an experimental manipulation for perceived personal relevance, I acknowledge the limitations of that test, namely its statistical power and the fact that the survey data does not confirm the effectiveness of the manipulation in altering the moderating variable. Therefore, future studies may explore different, possibly stronger experimental manipulations for perceived personal relevance. 

The results of this study, if generalizable, have significant implications. They suggest that citizens are susceptible to persuasion by government branding and symbolic communication, even with regard to salient, contested policy issues that have significant consequences for their own lives. Accordingly, these findings suggest that the boundaries of persuasion via branding are much wider than expected. This entails that public organizations can affect citizens in a broad array of policy domains and contexts by investing in symbolic communications. Alongside the benefits of this instrument in mitigating citizens' distrust, it also has the potency to yield a distorted, emotive, overoptimistic view of government organizations and their policies, which may undermine basic democratic accountability mechanisms. These concerns become even more prevalent and serious in the age of digital communication and artificial intelligence technologies, which enable organizations to engage with targeted audiences more easily, and to adapt messages and symbols to groups and individuals in a manner that would trigger their emotional responses more effectively. For good or for ill, citizens are affected by the emotional connections and associations of symbols. 


<br>

## Appendix A: Symbols manipulation
![](C:/SAAR/UNIVERSITY/R/SVIVA/papers/paper3_PA/revision/myfigures/symbols_manipulation.png) 

<font size="0.5">

*Note: The policy plans communications presented here are the strong policies, in the version adjusted for mobile interface.*  

</font>

<br>


## Appendix B: Information manipulation

**Haifa Bay air-pollution policy** (Reducing the air-pollution in the Haifa Bay)

EPM is working to reduce the air-pollution in the Haifa Bay by: 

*Strong*

* **Increasing the supervision of the factories in Haifa Bay**.
In order to reduce the emissions of pollutants from the factories in the industrial area, the Ministry is working to increase the supervision of the factories in several ways. The ministry will increase the number of inspection visits, expand the use of unannounced inspections, and increase the level of fines for polluters.

* **Reduction of emissions from vehicles in Haifa Bay**.
The Ministry is taking a number of actions to reduce the amount of air-pollution from vehicles in the area. The ministry promotes the installation of particle filters in hundreds of vehicles in the area, and the designation of a "clean-air district" from which polluting vehicles are banned.

*Weak*

* **Decreasing the supervision of factories in Haifa Bay**.
The Ministry is preparing for a change in the method of supervision of the factories. The ministry will reduce the number of inspection visits and unannounced inspections, rely on factories' self-reports on pollutant emissions, and reduce the level of fines. The change is coordinated with members of the industry, which are committed to reducing the pollution, and it will assist to build mutual respect between them and the ministry.

* **Raising citizens' awareness of the efforts to improve air quality**.
In recent years, the Ministry has taken steps to reduce air pollution in Haifa Bay, which have been partially successful. The Ministry is working to raise awareness of its efforts and to emphasize their success in order to improve its image among the residents. To this end, the ministry has increased its investment in public relations and publicity on this subject.

<br>

**Recycling policy** (Reducing waste and increasing recycling)

EPM is working to reduce the amount of waste and increase recycling through:

*Strong*

* **Packaging recycling - Increased supervision of manufacturers**.
In order to reduce the amount of waste generated from packaging and to encourage recycling, the Ministry implements the Packaging act, under which manufacturers and importers are responsible for recycling their packaging materials. The Ministry will work to increase enforcement by virtue of this act, and will enforce it for additional manufacturers and importers.

* **Reducing the usage of disposable bags**.
In order to reduce the use of disposable plastic bags, the Ministry has enforced the Carrier Bags act, which sets a compulsory charge on bags sold in large retail chains and imposes reporting obligations on the retail chains themselves. The Ministry is working to implement the act, and to encourage the transition to reusable grocery bags.

*Weak*

* **Packaging recycling - Decreased supervision of manufacturers**.
The Ministry is preparing to a change in the way manufacturers, who are responsible for the recycling of product packaging, are supervised. The ministry will reduce the number of inspections, rely on manufacturers' own reporting regarding their compliance with the requirements of the act, and will reduce the level of fines. The change is coordinated with industry representatives, who are committed to the issue, and it will assist to strengthen mutual respect between them and the ministry.

* **Reducing the usage of disposable bags**.
In recent years, the Ministry has taken steps to encourage the reduction of waste, which have been partially successful. The Ministry works to raise awareness of the actions taken and to highlight their success in order to improve its image among the public. To this end, the ministry has increased its investment in public relations and publicity in this subject.

<br>




# References













